Most organizations begin their generative AI journey the same way: they embed a knowledge corpus and provide the most relevant chunks to a large language model at inference time. This pattern is effective, but it has a natural ceiling. In most cases, that limit becomes apparent with the second or third use case—not the first.
Signs You’ve Outgrown Traditional RAG
If your workflow requires multiple retrieval steps, or if it must determine which source to consult next based on intermediate results, a static retrieve-then-generate pipeline is no longer sufficient. These scenarios benefit from models that can plan, invoke tools, reason over intermediate outputs, and adapt their next action accordingly.
Where Agents Deliver the Greatest Value
Agents excel when the output of each step directly influences the next. Typical examples include:
- Underwriting and claims triage that require sequential decision-making.
- Multi-table data investigations spanning different systems.
- Customer service workflows orchestrating actions across multiple backend applications.
Adopting agents also introduces new engineering considerations. Teams need well-defined tool interfaces, robust evaluation strategies for continuously evolving behaviors, and observability that captures intermediate reasoning and tool execution—not just the final response.
A simple rule of thumb is this: choose an agent when retrieval is only one of several decisions your AI system needs to make, rather than the entire solution.